PD-ADSV: An automated diagnosing system using voice signals and hard voting ensemble method for Parkinson’s disease

Ghaheri, Paria; Shateri, Ahmadreza; Nasiri, Hamid

doi:10.1016/j.simpa.2023.100504

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…Although both LightGBM and XGBoost [38] are capable of doing parallel arithmetic, LightGBM is superior to XGBoost due to its faster training speed and lower memory occupation, both of which help lower the communication cost of parallel learning [66]. The gradient-based one-side sampling (GOSS) decision tree algorithm, exclusive feature bundling (EFB), depth-limited histogram, and leaf-wise growth approach are the significant features of LightGBM [67]. GOSS can strike a compromise between the number of samples and the precision of the LightGBM decision tree.…”

Section: Lightgbmmentioning

confidence: 99%

Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Farzipour,

Elmi,

Nasiri

2023

Diagnostics

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The monkeypox virus poses a novel public health risk that might quickly escalate into a worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, finding tumor cells, and finding COVID-19 patients. In this study, we have created a dataset based on the data both collected and published by Global Health and used by the World Health Organization (WHO). Being entirely textual, this dataset shows the relationship between the symptoms and the monkeypox disease. The data have been analyzed, using gradient boosting methods such as Extreme Gradient Boosting (XGBoost), CatBoost, and LightGBM along with other standard machine learning methods such as Support Vector Machine (SVM) and Random Forest. All these methods have been compared. The research aims to provide an ML model based on symptoms for the diagnosis of monkeypox. Previous studies have only examined disease diagnosis using images. The best performance has belonged to XGBoost, with an accuracy of 1.0 in reviews. To check the model’s flexibility, k-fold cross-validation is used, reaching an average accuracy of 0.9 in 5 different splits of the test set. In addition, Shapley Additive Explanations (SHAP) helps in examining and explaining the output of the XGBoost model.

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Section: Lightgbmmentioning

confidence: 99%

Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Farzipour,

Elmi,

Nasiri

2023

Diagnostics

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“…First, it can offer high performance for both regression and classification problems utilizing Taylor series expansions of the objective function, efficiently constructing boosted trees and working in parallel. Second, a multi-threading parallel computing model can also be automatically called in XGBoost, which is several times faster than the traditional ensemble learning models [61]. Finally, the regularisation term used in its objective function increases the generalisation ability, making it less likely that the model would be overfitted by data [62][63][64][65][66].…”

Section: Xgboostmentioning

confidence: 99%

Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates

Saberi,

Nasiri,

Ghorbani

et al. 2023

Materials

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Material properties, geometrical dimensions, and environmental conditions can greatly influence the characteristics of bistable composite laminates. In the current work, to understand how each input feature contributes to the curvatures of the stable equilibrium shapes of bistable laminates and the snap-through force to change these configurations, the correlation between these inputs and outputs is studied using a novel explainable artificial intelligence (XAI) approach called SHapley Additive exPlanations (SHAP). SHAP is employed to explain the contribution and importance of the features influencing the curvatures and the snap-through force since XAI models change the data into a form that is more convenient for users to understand and interpret. The principle of minimum energy and the Rayleigh–Ritz method is applied to obtain the responses of the bistable laminates used as the input datasets in SHAP. SHAP effectively evaluates the importance of the input variables to the parameters. The results show that the transverse thermal expansion coefficient and moisture variation have the most impact on the model’s output for the transverse curvatures and snap-through force. The eXtreme Gradient Boosting (XGBoost) and Finite Element (FM) methods are also employed to identify the feature importance and validate the theoretical approach, respectively.

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Voice Features Examination for Parkinson’s Disease Detection Utilizing Machine Learning Methods

Putri,

Mara,

Ismail

et al. 2024

Lecture Notes in Electrical Engineering

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PD-ADSV: An automated diagnosing system using voice signals and hard voting ensemble method for Parkinson’s disease

Cited by 7 publications

References 39 publications

Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates

Voice Features Examination for Parkinson’s Disease Detection Utilizing Machine Learning Methods

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